Data Management II

Learning objectives

By the end of the lecture, you will be able to …

  • Reshape data between wide and long formats
  • Reorder columns and rows
  • Combine and expand dataframes

Packages

Load the standard packages.

library(here)
library(tidyverse) 
library(haven) # not core tidyverse
library(gssr)
library(gssrdoc)
library(summarytools)

GSS Panel Data: Download

https://gss.norc.org/get-the-data/stata


Heads Up!

Save the unzipped file in your class data folder.

GSS Panel Data: Load

# Use here() to construct the file path
gss_panel.dta <- here("data", "GSS_2020_panel_stata_1a/gss2020panel_r1a.dta")

#load the data using `haven::read_dta()`
data <- read_dta(gss_panel.dta)

# Or, do both at the same time!
# data <- read_dta(here("data", "GSS_2020_panel_stata_1a/gss2020panel_r1a.dta"))

GSS 2016-2020 Panel Dataset

Study of former 2016 and 2018 GSS respondents were interviewed again in 2020

  • Variables from 2016 (Wave 1a) have _1a appended
  • Variables from 2018 (Wave 1b) have _1b appended
  • Variables from 2020 (Wave 2) have _2 appended

GSS 2016-2020 Panel Dataset

# A tibble: 10 × 7
     yearid year_1a year_1b year_2 age_1a    age_1b    age_2    
      <dbl>   <dbl>   <dbl>  <dbl> <dbl+lbl> <dbl+lbl> <dbl+lbl>
 1 20182183      NA    2018     NA NA(i)        52     NA(i)    
 2 20180711      NA    2018     NA NA(i)        19     NA(i)    
 3 20182189      NA    2018   2020 NA(i)        37        39    
 4 20160354    2016      NA     NA    56     NA(i)     NA(i)    
 5 20180452      NA    2018   2020 NA(i)        29        31    
 6 20181503      NA    2018   2020 NA(i)        58        60    
 7 20162744    2016      NA   2020    71     NA(i)        75    
 8 20160315    2016      NA   2020    69     NA(i)        73    
 9 20160170    2016      NA     NA    75     NA(i)     NA(i)    
10 20161888    2016      NA     NA    71     NA(i)     NA(i)

Manipulating Dataframes

Selection helpers

Match variables according to a given pattern.

  • starts_with(): Starts with an exact prefix.
  • ends_with(): Ends with an exact suffix.
  • contains(): Contains a literal string.
  • …
my_data <- data |>
  select(yearid, wtssnr_2, 
         starts_with("age_"), 
         starts_with("family16_"),
         starts_with("socfrend_"),
         starts_with("childs_")) 


# You can supply multiple prefixes or suffixes.
my_data <- data |>
  select(yearid, wtssnr_2, 
         starts_with(c("age_", "family16_", "socfrend", "childs"))
         )

my_data <- as_factor(my_data) # Apply labels to data

head() & tail()

Look at the first few column names and first few rows.

head(my_data, n = 5)
# A tibble: 5 × 14
    yearid wtssnr_2 age_1a age_1b age_2 family16_1a       family16_1b family16_2
     <dbl>    <dbl> <fct>  <fct>  <fct> <fct>             <fct>       <fct>     
1 20160001    1.44  47     <NA>   51    both own mother … <NA>        not avail…
2 20160002    0.722 61     <NA>   65    both own mother … <NA>        not avail…
3 20160003   NA     72     <NA>   <NA>  both own mother … <NA>        iap       
4 20160004    2.89  43     <NA>   47    mother only       <NA>        not avail…
5 20160005   NA     55     <NA>   <NA>  both own mother … <NA>        iap       
# ℹ 6 more variables: socfrend_1a <fct>, socfrend_1b <fct>, socfrend_2 <fct>,
#   childs_1a <fct>, childs_1b <fct>, childs_2 <fct>


Look at the first few column names and last few rows.

tail(my_data, n = 5)
# A tibble: 5 × 14
    yearid wtssnr_2 age_1a age_1b age_2 family16_1a family16_1b       family16_2
     <dbl>    <dbl> <fct>  <fct>  <fct> <fct>       <fct>             <fct>     
1 20182344   NA     <NA>   37     <NA>  <NA>        mother and stepf… iap       
2 20182345    0.995 <NA>   75     77    <NA>        both own mother … not avail…
3 20182346    0.995 <NA>   67     70    <NA>        both own mother … not avail…
4 20182347   NA     <NA>   72     <NA>  <NA>        both own mother … iap       
5 20182348   NA     <NA>   79     <NA>  <NA>        both own mother … iap       
# ℹ 6 more variables: socfrend_1a <fct>, socfrend_1b <fct>, socfrend_2 <fct>,
#   childs_1a <fct>, childs_1b <fct>, childs_2 <fct>

Reminder: Tidy data

This data is NOT tidy!
Some column names include values of a variable (survey year).

# A tibble: 15,645 × 7
     yearid wtssnr_2 panel age   family16                    socfrend     childs
      <dbl>    <dbl> <chr> <fct> <fct>                       <fct>        <fct> 
 1 20160001    1.44  1a    47    both own mother and father  several tim… 3     
 2 20160001    1.44  1b    <NA>  <NA>                        <NA>         <NA>  
 3 20160001    1.44  2     51    not available for this year several tim… 3     
 4 20160002    0.722 1a    61    both own mother and father  several tim… 0     
 5 20160002    0.722 1b    <NA>  <NA>                        <NA>         <NA>  
 6 20160002    0.722 2     65    not available for this year about once … 0     
 7 20160003   NA     1a    72    both own mother and father  <NA>         2     
 8 20160003   NA     1b    <NA>  <NA>                        <NA>         <NA>  
 9 20160003   NA     2     <NA>  iap                         <NA>         <NA>  
10 20160004    2.89  1a    43    mother only                 once or twi… 4     
# ℹ 15,635 more rows


This data is tidy!
Each variable in its own column, and each observation in its own row.

pivot_longer()

my_data_long <- my_data |>
  pivot_longer(
    cols = 3:14,
    names_to = "variable",
    values_to = "value")

head(my_data_long, n = 5)
1
cols = specifies which columns you want to turn into one
2
names_to = defines the name of the new variable containing the current variable names
3
values_to= defines the name of the new variable that takes in the values of the variables 
# A tibble: 5 × 4
    yearid wtssnr_2 variable    value                     
     <dbl>    <dbl> <chr>       <fct>                     
1 20160001     1.44 age_1a      47                        
2 20160001     1.44 age_1b      <NA>                      
3 20160001     1.44 age_2       51                        
4 20160001     1.44 family16_1a both own mother and father
5 20160001     1.44 family16_1b <NA>

This is also not tidy data!
The variable column contains the variable names & the panel ID.

separate()

my_data_long <- my_data |>
  pivot_longer(
    cols = c(-yearid, -wtssnr_2),
    names_to = "variable",
    values_to = "value") |>
  separate_wider_delim(variable,
                       delim = "_",
                       names = c("variable", "panel"))

head(my_data_long, n = 5)
1
Split the column variable into two using a delimiter
2
A string giving the delimiter between values
3
names specifies the two new column names 
# A tibble: 5 × 5
    yearid wtssnr_2 variable panel value                     
     <dbl>    <dbl> <chr>    <chr> <fct>                     
1 20160001     1.44 age      1a    47                        
2 20160001     1.44 age      1b    <NA>                      
3 20160001     1.44 age      2     51                        
4 20160001     1.44 family16 1a    both own mother and father
5 20160001     1.44 family16 1b    <NA>

This is still not tidy data!
The value variable is a mix of different types of values

pivot_wider()

my_data <- my_data |> # overwriting my_data
  pivot_longer(
    cols = c(-yearid, -wtssnr_2),
    names_to = "variable",
    values_to = "value") |>
    separate_wider_delim(variable, 
                         delim = "_", 
                         names = c("variable", "panel")) |>
  pivot_wider(
    names_from = variable,
    values_from = value)

head(my_data, n = 5)
1
Increasing the number of columns and decreasing the number of rows
2
Which column to get the name of the output columns
3
Which column to get the cell values from 
# A tibble: 5 × 7
    yearid wtssnr_2 panel age   family16                    socfrend      childs
     <dbl>    <dbl> <chr> <fct> <fct>                       <fct>         <fct> 
1 20160001    1.44  1a    47    both own mother and father  several time… 3     
2 20160001    1.44  1b    <NA>  <NA>                        <NA>          <NA>  
3 20160001    1.44  2     51    not available for this year several time… 3     
4 20160002    0.722 1a    61    both own mother and father  several time… 0     
5 20160002    0.722 1b    <NA>  <NA>                        <NA>          <NA>

Recode the reshaped variable

my_data <- my_data |>
  mutate(panel = case_when(
         panel == "1a" ~ 2016,
         panel == "1b" ~ 2018,
         panel == "2" ~ 2020,
         TRUE ~ NA_integer_))

head(my_data, n = 3)
# A tibble: 3 × 7
    yearid wtssnr_2 panel age   family16                    socfrend      childs
     <dbl>    <dbl> <dbl> <fct> <fct>                       <fct>         <fct> 
1 20160001     1.44  2016 47    both own mother and father  several time… 3     
2 20160001     1.44  2018 <NA>  <NA>                        <NA>          <NA>  
3 20160001     1.44  2020 51    not available for this year several time… 3

Heads Up!

family16 is a time-invariant variable.

relocate()

my_data <- my_data |> 
  relocate(panel)

head(my_data, n = 2)
# A tibble: 2 × 7
  panel   yearid wtssnr_2 age   family16                   socfrend       childs
  <dbl>    <dbl>    <dbl> <fct> <fct>                      <fct>          <fct> 
1  2016 20160001     1.44 47    both own mother and father several times… 3     
2  2018 20160001     1.44 <NA>  <NA>                       <NA>           <NA>  


my_data <- my_data |> 
  relocate(panel, .after = yearid)

head(my_data, n = 2)
# A tibble: 2 × 7
    yearid panel wtssnr_2 age   family16                   socfrend       childs
     <dbl> <dbl>    <dbl> <fct> <fct>                      <fct>          <fct> 
1 20160001  2016     1.44 47    both own mother and father several times… 3     
2 20160001  2018     1.44 <NA>  <NA>                       <NA>           <NA>

arrange()

my_data |> 
  arrange(panel) |>
  select(yearid, panel, age, family16)
# A tibble: 15,645 × 4
     yearid panel age   family16                  
      <dbl> <dbl> <fct> <fct>                     
 1 20160001  2016 47    both own mother and father
 2 20160002  2016 61    both own mother and father
 3 20160003  2016 72    both own mother and father
 4 20160004  2016 43    mother only               
 5 20160005  2016 55    both own mother and father
 6 20160006  2016 53    other                     
 7 20160007  2016 50    both own mother and father
 8 20160008  2016 23    both own mother and father
 9 20160009  2016 45    both own mother and father
10 20160010  2016 71    both own mother and father
# ℹ 15,635 more rows


my_data |> 
  arrange(desc(panel)) |>
  select(yearid, panel, age, family16)
# A tibble: 15,645 × 4
     yearid panel age   family16                   
      <dbl> <dbl> <fct> <fct>                      
 1 20160001  2020 51    not available for this year
 2 20160002  2020 65    not available for this year
 3 20160003  2020 <NA>  iap                        
 4 20160004  2020 47    not available for this year
 5 20160005  2020 <NA>  iap                        
 6 20160006  2020 <NA>  iap                        
 7 20160007  2020 <NA>  iap                        
 8 20160008  2020 27    not available for this year
 9 20160009  2020 49    not available for this year
10 20160010  2020 <NA>  iap                        
# ℹ 15,635 more rows

Joining Dataframes

appending v.s. merging

APPEND

add new observations (rows) to existing variables


MERGE

add new variables (columns) to existing observations (many merge types)

appending v.s. merging

Example datasets

dataframe 1

  coupleid  name age
1        2  John  42
2        1 Megan  36
3        3   Bin  38

dataframe 2

  coupleid    name age
1        1     Sue  40
2        3  Ye-jin  39
3        2 Chrissy  35


dataframe 3

  coupleid marstat numchild country
1        3       1        1 S.Korea
2        1       0        0      US
3        2       1        4      US

append data with bind_rows()

df_all <- bind_rows(df_partner1, df_partner2)

tibble(df_all)
# A tibble: 6 × 3
  coupleid name      age
     <dbl> <chr>   <dbl>
1        2 John       42
2        1 Megan      36
3        3 Bin        38
4        1 Sue        40
5        3 Ye-jin     39
6        2 Chrissy    35

merge data with joins

add columns from df1 to df2, matching observations based on the keys

  • left_join() keeps all observations in df1.
  • right_join() keeps all observations in df2.
  • full_join() keeps all observations in df1 and df2.
  • inner_join() only keeps observations from df1 that have a matching key in df2

merge data with left_join()

df_couples <- left_join(df_partner1, df_family, by = "coupleid")

tibble(df_couples)
# A tibble: 3 × 6
  coupleid name    age marstat numchild country
     <dbl> <chr> <dbl>   <dbl>    <dbl> <chr>  
1        2 John     42       1        4 US     
2        1 Megan    36       0        0 US     
3        3 Bin      38       1        1 S.Korea